This invention relates generally to remote sensing and in particular to high-resolution imaging.
Spatial resolution of images is limited by the fact that an infinitely small point in the object plane appears in the image plane as a point that is spread out, consistent with a two-dimensional point-spread function (PSF) of an imaging system. For linear imaging systems, an image-plane field is mathematically equal to a convolution of the object-plane field with a PSF of an imaging system. The problem is to determine the object-plane field when an image-plane field is known.
The common way to solve this kind of problem is to estimate a PSF theoretically and then to apply a deconvolution technique to determine the object-plane field; see, for example, U.S. Pat. No. 7,869,627 from Jan. 11, 2011 by Northcott et al.
Another way to solve the problem is to determine a PSF directly by an experiment. It can be done by a simulation of an infinitely small object and then recording its image. This image represents the PSF that is used in further deconvolution; see, for example, U.S. Pat. No. 6,928,182 from Aug. 9, 2005 by Chui et al.
It is important that deconvolution methods are noise sensitive: a small change in a PSF can lead to big changes in the calculated object-plane field; this is a so-called ill-posed problem. There are methods for solving such problems using some additional information; see, for example, Tychonoff A. N. and Arsenin V. Y., Methods for Incorrect Problems Solution, Nauka, Moscow, 1986 and Wiener N., The extrapolation, interpolation and smoothing of stationary time series, New York, John Wiley & Sons, 1949.
However, a pure theoretical estimation of a PSF as well as its direct experimental measurement often does not guarantee the needed precision of the PSF and does not provide needed information for solving the problem. As a result, calculations of the object-plane field in these cases can lead to unpredictable distortions.